code: def _apply_effect(
      evaluator: Callable[..., Dict[str, float]],
      df: pd.DataFrame,
      treatment_column: str,
      outcome_column: str,
  ) -> float:
      return evaluator(
          df, treatment_column, outcome_column, eval_name="effect"
      )["effect"]
  
file_path: fklearn/src/fklearn/causal/effects.py
imports: import pandas
  from toolz import curry
  from typing import Dict, Callable
  from fklearn.validation.evaluators import spearman_evaluator, correlation_evaluator, linear_coefficient_evaluator, exponential_coefficient_evaluator, logistic_coefficient_evaluator
label_desc: Use image node_id 1 for calling a global function with example usage: _apply_effect(evaluator, df, treatment_column, outcome_column) and returns: evaluator